Naya Baig

At Cushman & Wakefield, I architect a Databricks and Delta Lake lakehouse for batch and real-time financial analytics across multi-terabyte datasets. My distributed PySpark workloads process 200M+ records daily, reducing data latency from hours to minutes.

I build event-driven Kafka and Spark Structured Streaming pipelines for near-real-time insights, and I designed Bronze, Silver, and Gold processing layers to standardize data processing. Through partitioning and Z-ordering, I improved query performance by 45% and reduced costs by 30%.

Previously at Molina Healthcare, I designed secure Azure Data Factory and Databricks pipelines for healthcare systems, integrating EHR, claims, and patient data into unified analytics platforms. I supported HIPAA-compliant governance, improved monitoring to reduce production incidents by 40%, and enabled curated datasets for machine learning and advanced analytics.

Across my work at InnoData Systems and in independent projects, I’ve built Python, SQL, and Spark ingestion workflows; observability frameworks; and streaming analytics pipelines. I focus on reliable, high-quality data products, proactive alerting, and practical performance optimization.